bioRxiv Subject Collection: Neuroscience's Journal

Fatigue >12 weeks after coronavirus disease (COVID) is associated with reduced reward sensitivity during effort-based decision making
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The neural mechanisms of concept formation over time in music
The interplay between abstraction, learning and memory is crucial for humans to interact with the environment. Abstraction in relation to concept formation is typically studied with Gestalt stimuli varying in their physical properties while maintaining abstract internal relations. The role of temporal integration in recognising abstract concepts has been, however, overlooked even in a sensory domain relying on temporal item succession such as audition. Here, we investigated the neural mechanisms underlying abstract concept generalisation over time using a musical adaptation of the old/new recognition paradigm. During magnetoencephalography (MEG) recordings, 108 participants categorised five-tone sequences as previously learned or novel or as musical or not. The sequences varied in pace, ranging from very fast (125 ms per sound) to very slow (5000 ms per sound). On the behavioural level, sequence duration exerted a substantial impact on the recognition and musicality judgments of the novel sequences. The differences in neural responses between memorised and novel sequences especially involved regions in the auditory cortex, medial cingulate gyrus and medial temporal lobe and were most pronounced at the highest musical pace, diminishing proportionally as the pace decreased. These findings contribute to our understanding of how the brain generalises and derives meaning from abstract concepts that unfold over time learned through the auditory system.

The Infraslow Fluctuation of Sigma Power During Sleep: Links to Markers of Arousal and Memory Reactivation Across Development
Sleep is both a state of disconnection from the environment and a critical period for restoration. But how does sleep balance responsiveness with the protection of key functions? The infraslow fluctuation of sigma power (ISFS) - the clustering of sleep spindles over 10-100 seconds - is thought to regulate this trade-off in rodents. However, the organization of arousal and memory reactivation markers within the human ISFS and its conservation in younger ages remain unclear. This study characterizes the ISFS from childhood to young adulthood (N = 154; ages 8-26), examining its relationship with functional markers. Results indicate that the ISFS is present across all ages, with frequency, variability, and strength increasing from early to late adolescence. Notably, markers of arousal and memory reactivation are organized within the spindle-rich ISFS peak. The consistent presence and organization of the ISFS suggest it is intrinsic to sleep, with adolescence marking a dynamic window. These insights may guide interventions to promote healthier sleep across development.

A Prefrontal Cortex Map based on Single Neuron Activity
The intrinsic organization underlying the central cognitive role of the prefrontal cortex (PFC) is poorly understood. The work to date has been dominated by cytoarchitecture as a canvas for studies on the PFC, constraining concepts, analyses, results, and their interpretations to pre-configured delimitations that might not be relevant to function. We approached organization by profiling the activity and spatial location of >23,000 neurons recorded in awake mice. Regularly firing neurons were over-represented in most PFC subregions, yet a fine-grained activity map of the PFC did not align with cytoarchitecturally defined subregions. Instead, we observed a robust relationship between spontaneous activity patterns and intra-PFC hierarchy, suggesting internal organization principles transcending cytoarchitecture. Single neuron responses to sounds did not reflect intra-PFC hierarchy but were linked to spontaneous firing rate, indicating that responsiveness increases with excitability and is decoupled from the PFC's intrinsic operational structure. Our data-driven approach provides a scalable roadmap to explore functional organizations in diverse brain regions and species, opening avenues to link activity, structure, and function in the brain.

A sensitive period for the development of episodic-like memory in mice
Episodic-like memory is a later-developing cognitive function supported by the hippocampus. In mice, the formation of extracellular perineuronal nets in subfield CA1 of the dorsal hippocampus controls the emergence of episodic-like memory during the fourth postnatal week (Ramsaran et al., 2023). Whether the timing of episodic like memory onset is hard-wired, or flexibly set by early life experiences during a critical or sensitive period for hippocampal maturation, is unknown. Here, we show that the trajectories for episodic-like memory development vary for mice given different sets of experiences spanning the second and third postnatal weeks. Specifically, episodic like memory precision developed later in mice that experienced early-life adversity, while it developed earlier in mice that experienced early-life enrichment. Moreover, we demonstrate that early-life experiences set the timing of episodic-like memory development by modulating the pace of perineuronal net formation in dorsal CA1. These results indicate that the hippocampus undergoes a sensitive period during which early-life experiences determine the timing for episodic-like memory development.

Modulation of learning-dependent plasticity in the attention network
Neuromodulatory interventions have attracted attention for their potential to enhance cognitive functions, including attention and perceptual learning. However, a comprehensive understanding of how these techniques affect cortical activity and plasticity during learning remains limited. Our previous work has shown that bilateral transcranial random noise stimulation (tRNS) over the intraparietal sulcus (IPS), but not over active control areas, coupled with a multi-session attention-based perceptual learning paradigm increases functional connectivity between nodes of the dorsal attention network (DVAN, Conto' et al., 2021). In this follow-up study we examined the impact of the same multi-session stimulation protocol on BOLD signal modulation in the DVAN during two interleaved attention tasks. Our findings reveal that tRNS targeting the intraparietal sulcus (IPS), a core node of the DVAN, selectively modulates BOLD responses during an orientation discrimination (OD) task, but not during a temporal order judgment (TOJ) task, emphasizing task-specific neural effects. Control analyses within the default mode network (DMN) showed no significant changes, indicating the specificity of tRNS effects on the DVAN. In contrast to existing neuroimaging studies on the cortical response to current stimulation, which often focus on single-session effects, our research measured long-term impacts of multi-session tRNS on cortical dynamics. Notably, we observed a significant increase in BOLD signal in the DVAN during OD following parietal tRNS, with increased beta weights compared to sham or to the human middle temporal area (hMT), our active control stimulation condition. These findings suggest that tRNS, when applied to critical nodes like the IPS, can enhance neural activity across the attention network, thus facilitating task performance. Our results further underscore tRNS's task-selective and network-specific effects, as neural modulation was observed exclusively in the spatial but not in the temporal processing task. The region-specific nature of these effects suggests that tRNS-induced modulation is constrained to key network nodes, enhancing the DVAN's cortical response in spatial tasks, a response that instead decreases after sham and has no benefit on behavior. The selectivity of the response aligns with theories of neural efficiency, indicating that tRNS may prevent natural downregulation in task-relevant areas and promotes sustained plasticity. These findings have important implications for using tRNS in cognitive training, highlighting its potential to facilitate targeted cognitive enhancement in both healthy and clinical populations.

When Little Means a Lot: Impact of a Brief Early-life Motor Training on Mouse Neuromuscular Development.
In this study, we aimed to determine the impact of an increase in motor activity during the highly plastic period of development of the motor spinal cord and hindlimb muscles in newborn mice. A swim training regimen, consisting of two sessions per day for two days, was conducted in 1 and 2-day-old (P1, P2) pups. P3-trained pups showed a faster acquisition of a four-limb swimming pattern, accompanied by dysregulated gene expression in the lateral motor column, alterations in the intrinsic membrane properties of motoneurons (MNs) and synaptic plasticity, as well as increased axonal myelination in motor regions of the spinal cord. Network-level changes were also observed, as synaptic events in MNs and spinal noradrenaline and serotonin contents were modified by training. At the muscular level, slight changes in neuromuscular junction morphology and myosin subtype expression in hindlimb muscles were observed in trained animals. Furthermore, the temporal sequence of acquiring the adult-like swimming pattern and postural development in trained pups showed differences persisting until almost the second postnatal week. A very short motor training performed just after birth is thus able to induce functional adaptation in the developing neuromuscular system that could persist several days. This highlights the vulnerability of the neuromuscular apparatus during development and the need to evaluate carefully the impact of any given sensorimotor procedure when considering its application to improve motor development or in rehabilitation strategies.

Human cerebellum and ventral tegmental area interact during extinction of learned fear
The key elements for fear extinction learning are unexpected omissions of expected aversive events, which are considered to be rewarding. Given its reception of reward information, we tested the hypothesis that the cerebellum contributes to reward prediction error processing driving extinction learning via its connections with the ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day fear conditioning paradigm in a 7T MR scanner. The cerebellum and VTA were active during unexpected omissions of aversive unconditioned stimuli, particularly during initial extinction trials. Increased functional connectivity was observed between the cerebellum and VTA, indicating that the cerebellum could positively modulate VTA activity, which in turn might facilitate dopaminergic signaling during fear extinction learning. These results imply that an interaction between the cerebellum and VTA should be incorporated into the existing model of the fear extinction network.

TIA1 Mediates Divergent Inflammatory Responses to Tauopathy in Microglia and Macrophages
The RNA binding protein TIA1 is known to regulate stress responses. Here we show that TIA1 plays a much broader role in inflammatory cells, being required for the microglial sensome. We crossed TIA1 cKO mice (using a CX3CR1 driven cre element) to PS19 MAPT P301S tauopathy mice. The peripheral macrophages of TIA1 cKO mice exhibited a hyper-inflammatory phenotype with increased cytokine signaling, as expected. Surprisingly, the brains of these mice showed striking reductions in inflammation, including decreases in microglial inflammatory cytokines (TNF and IL-1{beta}) and sensome markers (CLEC7A, TREM2, ITGAX); these reductions were accompanied by corresponding decreases in tau pathology. Analysis of the brain TIA1 protein interactome identified brain selective TIA1 protein mediated pathways, including strong interactions with the microglial protein C1q, which directs pruning of dystrophic neurons. These results uncover a previously unknown regulatory role for TIA1 in microglial activation in the context of neurodegenerative disease and highlights the divergent regulation of two mononuclear phagocytic lineages: microglia and macrophages.

Rapid fluctuations in histamine associated with intake of nutritive and non-nutritive solutions
The neurotransmitter histamine is involved in control of food intake, yet its dynamics during individual feeding episodes remain unexplored. Therefore, we used the novel genetically-encoded histamine sensor, HisLightG, combined with fiber photometry to measure histamine release in two hypothalamic regions critical in the food-suppressive effects of histamine, the paraventricular nucleus of the hypothalamus (PVH), and the ventromedial hypothalamus (VMH). Male mice were tested under different conditions to assess whether hunger, time of day, or the caloric content of the solution they were given affected histamine fluctuations. We found that histamine levels changed rapidly in response to eating, with reductions in histamine release occurring towards the end of licking bouts. These histamine fluctuations were independent of the experimental conditions. Notable regional differences were identified, however, such that in the PVH histamine rebounded to baseline levels, whereas in the VMH histamine remained lower than baseline for at least 10 seconds after licking ceased. In a separate cohort of male and female mice, enhancing histamine tone via administration of a histamine precursor (L-histidine) reduced the number of licks across multiple sucrose concentrations. Together, these findings indicate that histaminergic activity is modulated rapidly during ingestive episodes, and that understanding these release patterns may give insight into histamine's role in appetite suppression.

SMAS: Structural MRI-Based AD Score using Bayesian VAE
This study introduces the Structural MRI based Alzheimer's Disease Score (SMAS), a novel index intended to quantify Alzheimer's Disease (AD) related morphometric patterns using a deep learning Bayesian supervised Variational Autoencoder (Bayesian sVAE). SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DELCODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DELCODE: r=0.50; ADNI: r=0.28), hippocampal volume (DELCODE: r=-0.44; ADNI: r=-0.66), and total grey matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD related brain atrophy. Moreover, our longitudinal studies suggest that SMAS may be useful for early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over a 36 month period, SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. Relevance map analysis revealed significant morphological changes in key AD related brain regions including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.

Recurrent Connectivity Shapes Spatial Coding in Hippocampal CA3 Subregions
Stable and flexible neural representations of space in the hippocampus are crucial for navigating complex environments. However, how these distinct representations emerge from the underlying local circuit architecture remains unknown. Using two-photon imaging of CA3 subareas during active behavior, we reveal opposing coding strategies within specific CA3 subregions, with proximal neurons demonstrating stable and generalized representations and distal neurons showing dynamic and context-specific activity. We show in artificial neural network models that varying the recurrence level causes these differences in coding properties to emerge. We confirmed the contribution of recurrent connectivity to functional heterogeneity by characterizing the representational geometry of neural recordings and comparing it with theoretical predictions of neural manifold dimensionality. Our results indicate that local circuit organization, particularly recurrent connectivity among excitatory neurons, plays a key role in shaping complementary spatial representations within the hippocampus.

Opposing effects of rewarding and aversive stimuli on D1 and D2 types of dopamine-sensitive neurons in the central amygdala
Dopamine-sensitive neurons are organized in two classes of cells, expressing D1- or D2- types of dopamine receptors, and are often mediating opposing aspects of reward-oriented behaviors. Here, we focused on dopamine-sensitive neurons in the central amygdala - a brain structure critically involved in processing emotion-related stimuli. We discovered that both dopamine receptor types are present in the central medial nucleus, while the lateral part is populated predominantly with DRD2 cells. Exposing mice to rewarding and aversive stimuli we studied DRD1 and DRD2 cells activity using in vivo two-photon calcium imaging in the CeM. We showed that cocaine and sugar predominantly increase the activity of DRD1(+) neurons and decrease DRD2(+) cells. Repeated exposure to cocaine, however, had the opposite effect on spontaneous excitatory synaptic transmission in the CeM than exposure to sugar. Quinine, an aversive stimulus, primarily engaged DRD2(+) neurons, activating predominantly those cells that were previously inhibited by sugar exposure. Our results show that though DRD1 and DRD2 populations are differentially engaged and regulated by appetitive/aversive stimuli, both participate in sugar, cocaine, and quinine processing.

Single-nuclei transcriptomes in the hypothalamus and POA of a highly social cichlid
How does complex behavior arise from the genome across biological levels? Specific cell types have evolutionarily conserved functional roles in regulating and maintaining various aspects of social behavior. There is a long history of research demonstrating how specific cells in the hypothalamus and POA are critical in regulating social status and reproductive behavior. This cell function is dependent not just on one gene, but the entire gene expression network working within each cell. The brain is a heterogenous tissue made up of a broad diversity of cells. Targeting the cellular-level transcriptomes in socially relevant brain regions enables the identification of social status mediators. Single-nuclei RNA-sequencing (snRNA-seq) provides the resolution to identify cell-specific transcriptomes, with more practical benefits compared to single-cell approaches. Single-nucleus sequencing of neuronal tissue has remained fairly limited to model mammalian organisms. Here we performed snRNA-seq in the hypothalamus and POA of Astatotilapia burtoni, a fish system ideal for social neuroscience. Male A. burtoni are characterized by plastic phenotypes associated with social status, dominant and subordinate, with well-established differences in neural gene expression patterns and specific neuron morphology. This provides a novel opportunity to apply snRNA-seq in a complex tissue of a non-model system using hypothesis driven approaches. We show how changes in social status are linked to distinct transcriptomic profiles at the cellular level.

Plug-and-Play Myoelectric Control via a Self-Calibrating Random Forest Common Model
Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behaviour variations, etc., substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage. However, the EMG characteristics could change even within a short period of time. Our objective is to develop a self-calibrating model, with an automatic and unsupervised self-calibration mechanism. Approach: We developed a computationally efficient random forest (RF) common model, which can (1) be pre-trained and easily adapt to a new user via one-shot calibration, and (2) keep calibrating itself once in a while by boosting the RF with new decision trees trained on pseudo-labels of testing samples in a data buffer. Main results: Our model has been validated in both offline and real-time, both open and closed-loop, both intra-day and long-term (up to 5 weeks) experiments. We tested this approach with data from 66 able-bodied participants. We also explored the effects of bidirectional user-model co-adaption in closed-loop experiments. We found that the self-calibrating model could gradually improve its performance in long-term use. With visual feedback, users will also adapt to the dynamic model meanwhile learn to perform hand gestures with significantly lower EMG amplitudes (less muscle effort). Significance: Our random forest-approach provides a new alternative built on simple decision tree for myoelectric control, which is explainable, computationally efficient, and requires minimal data for model calibration. Source codes are available at: https://github.com/MoveR-Digital-Health-and-Care-Hub/self-calibrating-rf.

Axonal injury signaling is restrained by a spared synaptic branch
The intrinsic ability of injured neurons to degenerate and regenerate their axons facilitates nervous system repair, however this ability is not engaged in all neurons and injury locations. Here we investigate the regulation of a conserved axonal injury response pathway with respect to the location of damage in branched motoneuron axons in Drosophila larvae. The dileucine zipper kinase DLK, (also known as MAP3K12 in mammals and Wallenda (Wnd) in Drosophila), is a key regulator of diverse responses to axonal injury. In three different populations of motoneurons, we observed the same striking result that Wnd/DLK signaling becomes activated only in response to injuries that remove all synaptic terminals. Injuries that spare even a small part of a synaptic terminal fail to activate Wnd/DLK signaling, despite the presence of extensive axonal degeneration. The regulation of injury-induced Wnd/DLK signaling occurs independently of its previously known regulator, the Hiw/PHR ubiquitin ligase. We propose that Wnd/DLK signaling regulation is linked to the trafficking of a synapse-to-nucleus axonal cargo and that this mechanism enables neurons to respond to impairments in synaptic connectivity.

Ultrastructural correlates of circadian structural plasticity
The central clock in the Drosophila's brain consists of approximately 250 neurons organized into a relatively complex network. Within this network, four small Lateral ventral Neurons (s-LNvs) in each hemisphere are rhythmically loaded with the neuropeptide pigment dispersing factor (PDF), which plays a crucial role in synchronizing the calcium rhythms across the network. Additionally, there are daily variations in markers of active synaptic sites at their terminals, along with changes in synaptic partners. The s-LNv axo-dendritic arbor undergoes characteristic remodeling in sync with these events, a process known as circadian structural plasticity. The relationship between these various plastic changes remains unclear, largely due to a lack of techniques that offer precise, comprehensive insights into both connectivity and structure. In this study, we genetically labeled the mitochondrial matrix of the s-LNvs using APEX2 peroxidase expression, enabling us to generate three Serial Block-face Scanning Electron Microscopy (SBEM) volumes where the s-LNvs were recognizable. Each volume represents a distinct time point in the circadian remodeling of the s-LNv terminals: ZT22 (two hours before dawn), ZT2 (two hours after dawn), and ZT14 (two hours after dusk). By tracing the labeled mitochondria, we identified and segmented the terminals of four s-LNvs in each brain. We then manually segmented dense core vesicles, both free-floating and those fusing to the membrane, as well as pre-synaptic sites and mitochondria. We discovered that dense core vesicles (DCVs) are accumulated and released from the s-LNv varicosities with a peak in the morning. Additionally, we observed nearly 50% more presynaptic sites in the morning compared to nighttime. These changes are accompanied by a marked shift in mitochondrial shape and volume, indicating functional differences between day and night. Finally, these adjustments are coordinated with changes in neurite length and the number of varicosities. We propose that structural plasticity helps organize daily changes in functional units, dynamizing the impact of these neurons on the network.

Transcriptome-wide mapping of small ribosomal subunits elucidates scanning mechanisms of translation initiation in the mammalian brain
Protein synthesis in neurons is highly compartmentalised and regulated, with key roles for translation initiation and elongation factors. The most widely used transcriptome-wide method for measuring translation, ribosome profiling, characterises the elongation phase of translation but does not provide insight into the initiation phase with scanning of the small ribosomal subunit (SSU). Here, we adapted and optimised ribosome complex profiling (RCP-seq) for brain tissue, that captures SSUs and allows analysis of translation initiation dynamics in the adult murine dentate gyrus (DG) region of the hippocampus. We show an accumulation of SSUs upstream of the start codon specifically enriched on synaptically localised RNAs and extensive translation of 5 prime upstream open reading frames (uORFs), suggesting regulation both during scanning and transition to the elongation phase. We further demonstrate that neuron-specific transcripts are more expressed, scanned, and translated than glia-specific transcripts. Of the neuronal mRNAs, monosome-preferring mRNAs exhibited reduced scanning and elongation relative to polysome-preferring transcripts implying reduced recruitment of ribosomes. In sum, RCP-seq elucidates translation initiation dynamics in the mammalian brain and uncovers cell-type- and transcript-specific regulation.

Response of neuronal populations to phase-locked stimulation: model-based predictions and validation
Background: Modulation of neuronal oscillations holds promise for the treatment of neurological disorders. Nonetheless, stimulating neuronal populations in a continuous open-loop manner can lead to side effects and suboptimal efficiency. Closed-loop strategies such as phase-locked stimulation aim to address these shortcomings by offering a more targeted modulation. While theories have been developed to understand the neural response to stimulation, their predictions have not been thoroughly tested using experimental data. Objective: We aimed to test the predictions of a mathematical model regarding the response of neuronal populations to phase-locked stimulation. Methods: Using a coupled oscillator model, we expanded on two key predictions describing the response to stimulation as a function of the phase and amplitude of ongoing neural activity. To investigate these predictions, we analyzed electrocorticogram (ECoG) recordings from a previously conducted study in Parkinsonian rats, and extracted the corresponding phase and response curves. Results: We demonstrated that the amplitude response to stimulation is strongly correlated to the derivative of the phase response ( > 0.8) in all animals except one, thereby validating a key model prediction. The second prediction postulated that the stimulation becomes ineffective when the network synchrony is high, a trend that appeared missing in the data. Our analysis explained this discrepancy by showing that the neural populations in Parkinsonian rats did not reach the level of synchrony for which the theory would predict ineffective stimulation. Conclusions: Our results highlight the potential of fine tuning stimulation paradigms informed by mathematical models that consider both the ongoing phase and amplitude of the targeted neural oscillation.

Olfactory bulb tracks breathing rhythms and place in freely behaving mice
Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, to take full advantage of olfactory information, an animal's brain must contextualize odor-driven activity with information about when, where, and how they sniffed. To better understand contextual information in the olfactory system, we captured the breathing and movements of mice while recording from their olfactory bulb. In stimulus- and task-free experiments, mice structure their breathing into persistent rhythmic states which are synchronous with statelike structure in ongoing neuronal population activity. These population states reflect a strong dependence of individual neuron activity on variation in sniff frequency, which we display using ''sniff fields'' and quantify using generalized linear models. In addition, many olfactory bulb neurons have ''place fields'' that display significant dependence of firing on allocentric location, which were comparable with hippocampal neurons recorded under the same conditions. At the population level, a mouse's location can be decoded from olfactory bulb with similar accuracy to hippocampus. Olfactory bulb place sensitivity cannot be explained by breathing rhythms or scent marks. Taken together, we show that the mouse olfactory bulb tracks breathing rhythms and self-location, which may help unite internal models of self and environment with olfactory information as soon as that information enters the brain.

Unifying Subicular Function: A Predictive Map Approach
The successor representation has emerged as a powerful model for understanding mammalian navigation and memory; explaining the spatial coding properties of hippocampal place cells and entorhinal grid cells. However, the diverse spatial responses of subicular neurons, the primary output of the hippocampus, have eluded a unified account. Here, we demonstrate that incorporating rodent behavioural biases into the successor representation successfully reproduces the heterogeneous activity patterns of subicular neurons. This framework accounts for the emergence of boundary and corner cells; neuronal types absent in upstream hippocampal regions. We provide evidence that subicular firing patterns are more accurately described by the successor representation than a purely spatial or boundary vector cell model of subiculum. Our work offers a unifying theory of subicular function that positions the subiculum, more than other hippocampal regions, as a predictive map of the environment.

Employing biological sex as a primary variable can restrict our understanding of behavioral diversity (and sex differences) in psychostimulant activity.
Background: In addition to being biological sex groups, males and females are also individuals expressing behavioral diversity. Our MISSING (Mapping Intrinsic Sex Similarities as an Integral quality of Normalized Groups) model suggests that, with respect to behavior, grouping according to the individual yields groups with differences that exceed biological sex-related differences, but this needs further clarification. Thus, we hypothesized that, compared to the current model (grouping by biological sex), the MISSING model (grouping by individual attributes) was the more effective grouping strategy to identify behavioral diversity. Methods: We conducted experiments in rats to determine the locomotor activity (in 90 min) following intraperitoneal injections of saline (n = 12 males, n = 11 females) and cocaine (n = 8 males, n = 11 females). For the current model, we compared males versus females using unpaired t-tests. For the MISSING model, we identified clusters of individuals (males and females) using normal mixtures clustering analysis of several behavioral variables and employed unpaired t-tests to compare clusters and Two-way ANOVA to determine if there were any SEX by cluster interactions. For both models, we employed linear regression analysis to compare relationships between variables and Two-way repeated measures ANOVA to analyze locomotor activity time course. Results: For both the saline and cocaine groups, the MISSING model identified two behavioral clusters with differences that exceeded any differences due to biological sex. Conclusions: The MISSING model suggests that employing sex as a biological variable can limit our understanding of sex and individual differences in psychostimulant activity.

Are resting-state networks the brain's cognitive atoms? Differential dynamic reconfiguration of functional brain networks across tasks and at rest
It is increasingly popular to utilise functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging (fMRI) to characterize human functional brain networks and to use the emergent resting-state networks (RSNs) in basic and clinical neuroscience. Often, they are treated as 'atomic' building blocks that underpin human cognition. However, the true function of these RSNs, as well as the relationship between intrinsic and task-evoked functional brain networks, are complex and incompletely characterized. Here, we investigated the functional characteristics of the intrinsic and extrinsic networks using resting-state and task fMRI. Independent component analysis (ICA) was used to estimate spatiotemporal functional networks during tasks and at rest, and to compare the spatiotemporal properties of each network. While there was some spatial correspondence between the RSNs and task-evoked networks, our results demonstrated that the task-evoked functional networks were different from the RSNs in task-relatedness as well as spatial topology. Furthermore, the degree of topological differences between the RSNs and task-evoked networks was modulated by a given task. Comparison between the RSNs and task-evoked networks showed that tasks reconfigure the RSNs by changing FC with various brain regions specific to the task condition. Our findings indicate that the brain does not maintain an "invariant intrinsic" network architecture when it engages in a task. Instead, the tasks reconfigure the network architectures, thereby accommodating specific computational/representational task requirements through flexible interactions between demand-specific regions. Thus, the results suggest that task fMRI is required to understand the full repertoire of the brain's functional architecture.

Astrocytic modulation of population encoding in mouse visual cortex via GABA transporter 3 revealed by multiplexed CRISPR/Cas9 gene editing
Astrocytes, which are increasingly recognized as pivotal constituents of brain circuits governing a wide range of functions, express GABA transporter 3 (Gat3), an astrocyte-specific GABA transporter responsible for maintenance of extra-synaptic GABA levels. Here, we examined the functional role of Gat3 in astrocyte-mediated modulation of neuronal activity and information encoding. First, we developed a multiplexed CRISPR construct applicable for effective genetic ablation of Gat3 in the visual cortex of adult mice. Using in vivo two-photon calcium imaging of visual cortex neurons in Gat3 knockout mice, we observed changes in spontaneous and visually driven single neuronal response properties such as response magnitudes and trial-to-trial variability. Gat3 knockout exerted a pronounced influence on population-level neuronal activity, altering the response dynamics of neuronal populations and impairing their ability to accurately represent stimulus information. These findings demonstrate that Gat3 in astrocytes profoundly shapes the sensory information encoding capacity of neurons and networks within the visual cortex.

Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace
Sensory processing arises from the communication between neural populations across multiple brain areas. While the widespread presence of neural response variability shared throughout a neural population limits the amount of stimulus-related information those populations can accurately represent, how this variability affects the interareal communication of sensory information is unknown. We propose a mathematical framework to understand the impact of neural population response variability on sensory information transmission. We combine linear Fisher information, a metric connecting stimulus representation and variability, with the framework of communication subspaces, which suggests that functional mappings between cortical populations are low-dimensional relative to the space of population activity patterns. From this, we partition Fisher information depending on the alignment between the population covariance and the mean tuning direction projected onto the communication subspace or its orthogonal complement. We provide mathematical and numerical analyses of our proposed decomposition of Fisher information and examine theoretical scenarios that demonstrate how to leverage communication subspaces for flexible routing and gating of stimulus information. This work will provide researchers investigating interareal communication with a theoretical lens through which to understand sensory information transmission and guide experimental design.

Differentiating hallucination proneness dimensions through resting state alpha dynamics
Background: Hallucination-like experiences (HLEs) are untriggered sensory perceptions linked to externalizing bias - the misattribution of self-generated sensory experiences to an external source. The vulnerability to HLEs, i.e., hallucination proneness (HP), is typically assessed by the Launay-Slade Hallucination Scale (LSHS). A recent LSHS factor analysis revealed four distinct HP dimensions: Multisensory HLEs, Auditory daydreaming, Vivid thoughts and inner speech, and Personified HLEs. The current study assesses whether these HP dimensions map onto distinct patterns of resting state brain dynamics in the alpha frequency band due to its modulatory role in attention and perception. Methods: We used a Hidden semi-Markov Model to segment continuous RS alpha activity into nine recurrent brain states and extracted the total number of transitions (TT) and the number of visits per state (SV). We assessed how the HP dimensions relate to these metrics, calculated across the entire 3-minute recording and within shorter sliding windows to capture finer temporal changes. Results: All HP dimensions and increased RS time correlated with increased TT. Increased Personified HLEs scores linked to different time-dependent changes of SV in two states (SV to state 5 decreased over time, while visits to state 9 increased), highlighting distinct alpha dynamics in high- and low-hallucination prone individuals. Conclusions: Increased TT could indicate frequent attentional switches between internal and external states. Different SV patterns related to higher Personified HLEs scores suggest unstable source monitoring, potentially inducing an externalizing bias. These findings provide novel predictors of HP dimensions, revealing distinct neural profiles associated with different vulnerability profiles.

A chill brain-music interface for enhancing music chills with personalized playlists
Music chills are pleasurable experiences while listening to music, often accompanied by physical responses, such as goosebumps1,2. Enjoying music that induces chills is central to music appreciation, and engages the reward system in the brain3-5. However, the specific songs that trigger chills vary with individual preferences6, and the neural substrates associated with musical rewards differ among individuals7-9, making it challenging to establish a standard method for enhancing music chills. In this study, we developed the Chill Brain-Music Interface (C-BMI), a closed-loop neurofeedback system that uses in-ear electroencephalogram (EEG) for song selection. The C-BMI generates personalized playlists aimed at evoking chills by integrating individual song preferences and neural activity related to music reward processing. Twenty-four participants listened to both self-selected and other-selected songs, reporting higher pleasure levels and experiencing more chills in their self-selected songs. We constructed two LASSO regression models to support the C-BMI. Model 1 predicted pleasure based on the acoustic features of the self-selected songs. Model 2 classified the EEG responses when participants listened to self-selected versus other-selected songs. Model 1 was applied to over 7,000 candidate songs, predicting pleasure scores. We used these predicted scores and acoustic similarity to the self-selected songs to rank songs that were likely to induce pleasure. Using this ranking, four tailored playlists were generated. Two playlists were designed to augment pleasure by selecting top-ranked songs, one of which incorporated real-time pleasure estimates from Model 2 to continuously update Model 1 and refine song rankings. Additionally, two playlists aimed to diminish pleasure, with one updated using Model 2. We found that the pleasure-augmenting playlist with EEG-based updates elicited more chills and higher pleasure levels than pleasure-diminishing playlists. Our results indicate that C-BMI using in-ear EEG data can enhance music-induced chills.

Sex differences in the impact of nerve injury on Locus Coeruleus function and behaviour in mice
Chronic pain often coexists with stress-related disorders such as anxiety and depression, with a higher prevalence in women. Rodent studies reveal significant sex differences in pain and stress-related behaviours, emphasising the need to explore underlying neurobiological mechanisms. The locus coeruleus (LC), the main noradrenergic nucleus in the brainstem, plays a crucial role in modulating pain and stress responses and exhibits sex-specific characteristics. In this study, we investigated the effects of neuropathic pain on the LC in male and female mice, focusing on sensory thresholds, emotional states, and cognitive function. Nerve injury caused immediate sensory hypersensitivity in both sexes, while depressive-like behaviours and cognitive impairments emerged only after prolonged injury. Interestingly, anxiety-like behaviours and heightened fear conditioning were exclusive to males, which correlated with specific structural and functional changes in the male LC, such as increased noradrenergic cell counts, larger somato-dendritic volume, and greater neuron excitability. In females, however, neuronal excitability was reduced. Chemogenetic inhibition of LC neurons alleviated depressive- and anxiety-like behaviours, as well as fear conditioning, but only in males. Overall, neuropathic pain induces distinct behavioural and neurobiological responses in males and females, challenging traditional views on female vulnerability to pain-induced anxiety and highlighting the need for sex-specific LC-targeted therapies.

Personalized tDCS targeting visual motion area V5 modulates smooth pursuit initiation
Smooth pursuit eye movements provide an ideal model of sensorimotor integration with visual motion area V5 being a key candidate integrating visual motion processing with oculomotor control. We applied personalized transcranial direct current stimulation (tDCS) explicitly targeting individual V5 to induce subtle impairment or facilitation of sensorimotor integration during pursuit and assess the gain by personalized, compared to conventional normative tDCS. Pursuit initiation was specifically delayed during personalized cathodal tDCS targeting right V5 pointing towards the involvement of specific functional subareas of V5. The results were well-controlled by anodal and sham tDCS, different pursuit tasks, and by two control experiments, one that applied personalized tDCS targeting FEF and another that applied normative tDCS over V5, for all of which no pursuit modulation was observed. Importantly, in contrast to normative tDCS, personalized tDCS effectively modulated pursuit by adapting electric fields to individual anatomical and neurophysiological V5 properties, yielding interesting potential for the improved effectiveness of personalized tDCS in general.

Cocaine, via ΔFosB, remodels gene expression and excitability in ventral hippocampus
Ventral hippocampus (vHPC) CA1 pyramidal neurons send glutamatergic projections to nucleus accumbens (NAc), and this vHPC-NAc circuit mediates cocaine seeking and reward, but it is unclear whether vHPC-NAc neuron properties are modulated by cocaine exposure to drive subsequent behavior. The immediate early gene transcription factor {Delta}FosB is induced throughout the brain by cocaine and is critical for cocaine seeking, but it's function in vHPC-NAc neurons is not understood. We now show that circuit-specific knockout of {Delta}FosB in vHPC-NAc neurons impaired cocaine reward expression and forced abstinence-induced seeking. We also found that vHPC-NAc excitability was decreased by experimenter-administered repeated cocaine and cocaine self-administration, and this cocaine-induced excitability decrease was mediated by {Delta}FosB expression. To uncover the mechanism of this change in circuit function, we used circuit-specific translating ribosomal affinity purification (TRAP) to assess cocaine-induced, {Delta}FosB-dependent changes in gene expression in vHPC-NAc. We found that cocaine causes a {Delta}FosB-dependent increase in the expression of calreticulin, an ER-resident calcium-buffering protein. Calreticulin expression mediated vHPC-NAc excitability and was necessary for cocaine reward. These findings uncover a novel, non-canonical mechanism by which cocaine increases calreticulin in vHPC leading to decreased vHPC-NAc excitability and drives cocaine seeking and reward.

Gut Microbiota Regulates Exercise-induced Hormetic Modulation of Cognitive Function
Lifestyle factors, particularly physical exercise, significantly influence brain structure and cognitive function through a hormetic effect dependent on exercise intensity and duration. The underlying mechanisms of this profile remain largely unexplored. Recently, the gut microbiota, has emerged a potent modulator of lifestyle-induced changes on brain and behavior. Here, we demonstrate that a 40-minute protocol of moderate exercise enhances cognitive abilities related to object recognition and memory, and increases hippocampal neurogenesis in adult mice compared to sedentary controls, but these cognitive and neurogenic benefits vanish when the exercise intensity or duration is increased. Furthermore, we identified significant alpha- and beta-diversity changes and distinct bacteria composition profiles of gut microbiota associated with different exercise regimens. Specific bacterial families showed altered relative abundances depending on exercise intensity and duration, with certain families' quantities significantly correlating with cognitive performance (Angelakisella, Acetatifactor, Erysipelatoclostridium, and Coriobacteriaceae UCG-002.). To parse causal mechanisms, fecal microbiota transplantation from exercised to sedentary mice replicated the cognitive and brain structural improvements observed in the donor animals. These findings suggest that the hormetic effects of physical exercise on cognitive function and neurogenesis are mediated by corresponding changes in the gut microbiota, indicating a novel mechanistic link between exercise, brain, and gut microbiota composition.

Real-Time Spatiotemporal Filtering for Artifact-Free EEG during Electrical Neurostimulation
Combining electrical neurostimulation with electroencephalography (EEG) for adaptive neurostimulation remains challenging due to the presence of stimulation artifacts in the recorded signal. Interpretation of EEG activity concurrent with stimulation requires real-time filtering of this noisy signal. While traditional frequency domain filters can suppress activity within frequency bands, they fail to differentiate sources in situations when there is a shared frequency characteristic between brain activity and stimulation signal. Here we present a new real-time denoising approach that combines spatial filtering and dynamic filter application. The spatiotemporal filter can suppress stimulation artifacts that share frequency bands with the brain signal in real-time while preserving the full spectral power. The spatial filter is also dynamically updated to account for possible changes in artifact topography. Finally, the filter is robust to changes of stimulation intensity, frequency and duration, enabling reliable denoising performance for inclusion in brain state-based closed-loop stimulation.

Inhibition and Updating Share Common Resources: Evidence from Drift Diffusion Model
Inhibition and updating are fundamental cognitive functions in humans, yet the nature of their relationship, whether shared or distinct, remains ambiguous. This study investigates the relationship between inhibition and updating and examines whether this relationship is altered in individuals with trait anxiety. A novel paradigm that combines N-back and congruent/incongruent Stroop tasks were utilized to explore these issues by employing Signal Detection Theory (SDT) and the hierarchical drift diffusion model (HDDM) methods. The results revealed that participants exhibited longer response times and higher error rates in conditions requiring both inhibition and updating compared to those requiring updating alone. SDT analysis indicated lower discriminability, while HDDM analysis showed slower drift rates and longer non-decision times. These findings suggest that inhibition consumes cognitive resources, leading to worse updating performance, implying that both functions may rely on shared resources. In the anxiety group, higher trait anxiety scores correlated with increased response times and decreased accuracy. The drift rate fully mediated the effect of trait anxiety on accuracy, suggesting that the speed of effective information accumulation is a key mechanism underlying the abnormal functioning of inhibition and updating in individuals with anxiety. Overall, the results elucidate the relationship between these fundamental cognitive functions, supporting the notion that inhibition and updating share cognitive resources. Furthermore, this study provides evidence and insights into the cognitive abnormalities observed in individuals with anxiety.

Resting-state connectivity modifies the effects of amyloid on cognitive and physical function: evidence for network-based cognitive reserve
Cognitive and physical function are interrelated in aging co-occurring impairments in both domains can be debilitating and lead to increased risk of developing dementia. Amyloid beta (A{beta}) deposition in the brain is linked to cognitive decline and is also associated with poorer physical function in older adults. However, significant inter-individual variability exists with respect to the influence of increased brain A{beta} concentrations on cognitive and physical outcomes. Identifying factors that explain inter-individual variability in associations between A{beta} and clinical outcomes could inform interventions designed to delay declines in both cognitive and physical function. Cognitive reserve (CR) is considered a buffer that allows for cognitive performance that is better than expected for a given level of brain injury or pathology. Although the neural mechanisms underlying CR remain unknown, there is growing evidence that resting-state brain networks may serve as a neural surrogate for CR. The currently study evaluated whether functional brain networks modified associations between brain A{beta} and cognitive and physical function in community-dwelling older adults from the Brain Networks and Mobility (B-NET) study. We found that the integrity of the central executive and basal ganglia networks modified associations of A{beta} with cognitive and physical performance. Associations between brain A{beta} and cognitive and physical function were less pronounced when brain network integrity was high. The current study introduces novel evidence for brain networks underlying CR as a buffer against the influence of A{beta} accumulation on cognitive and physical function.

Proteostasis dysregulation in p.A53T-α-Synuclein iPSC-derived astrocytes exacerbates neurodegeneration in a Parkinson's disease model with Lewy-like pathology
Alpha-Synuclein (Syn) plays a central role in Parkinson's disease (PD) and the p.A53T mutation causes an early-onset familial form of PD with severe manifestations. The pathological effects of the p.A53T-Syn mutation have been extensively investigated in neurons, yet the consequences on astrocytes and astrocytic contribution to PD pathology are understudied. Here, we differentiated induced pluripotent stem cells from PD patients carrying the p.A53T-Syn mutation to astrocytes, which uncovered cell-intrinsic phenotypes, including calcium dyshomeostasis and accumulation of protein aggregates. Proteomic profiling and functional analyses revealed perturbed protein catabolic processes, involving the proteasome and autophagy, associated with lysosomal malfunction. Dopamine neurons co-cultured with p.A53T-Syn astrocytes displayed exacerbated neurodegeneration with hallmark Lewy-like pathologies, rescued by control astrocytes due to their ability to resolve neuronal Syn aggregates by endocytic clearance. Our findings underscore a critical impact of p.A53T-Syn on astrocytic protein quality control mechanisms, positioning astrocytes as important contributors to PD neuropathology.

Electrical Coordinated Reset Stimulation Induces Network Desynchronization in an in Vivo Model of Status Epilepticus
Epilepsy, a neurological disorder characterized by recurrent seizures, profoundly impacts individuals worldwide. Various electrical stimulation protocols have been investigated to mitigate epileptic seizures, among which Coordinated Reset (CR) stimulation may have potential for inducing long-lasting neural desynchronization. This study explores the acute effects of CR stimulation on synchronization dynamics during Status Epilepticus (SE) in an in vivo animal model. An electrographically sustained seizure-state was induced via 4-aminopyridine (4AP) administration to CA3. Custom-designed electrode probes were implanted to facilitate simultaneous recording and electrical stimulation. Analytical univariate and bivariate features were constructed from the LFP time-series recording. Feature metrics focused on spike synchronization metrics and continuous signal analysis of amplitude, spectral power and phase synchronization across electrode pairs and frequency bands. Significance of modulation was assessed through permutation testing of the observed differences between the CR-stimulated group (N=5) compared to the control (no stimulation) group (N=3) during SE. Results showed overall decrease in amplitude and power univariate features, and a significant modulation of bivariate synchronization and connectivity measures across the spectrum between the CR stimulation and control group. Our findings underscore the potential effectiveness of CR stimulation in attenuating excessive neural synchronization, paving the way for further exploration of CR stimulation as a viable intervention for network desynchronization of epileptiform activity and subsequently treatment of seizures.

Dynamic Causal Modeling in Probabilistic Programming Languages
Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magneto/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuroscience research and applications in neuroimaging through automatic DCM.

Single Objective Light Sheet Microscopy allows high-resolution in vivo brain imaging of Drosophila.
In vivo imaging of dynamic sub-cellular brain structures in Drosophila melanogaster is key to understanding several phenomena in neuroscience. However, a trade-off between spatial resolution, speed, photodamage, and setup complexity limits its implementation. Here, we designed and built a single objective light sheet microscope, customized for in vivo imaging of adult flies and optimized for maximum resolution. Unlike multi-objective light sheet setups, the microscope uses a single objective at the fly head interface, facilitating sample mounting and inspection, and reducing invasiveness. In contrast to two-photon microscopies, the light-sheet configuration with visible excitation offers reduced phototoxicity. We demonstrate in vivo imaging of the membrane, mitochondria, and dense-core vesicles in the axonal projections of small lateral ventral neurons. The achieved resolution was between 380 to 500 nm within a field of view of 70x50x12 m3. This unique combination of easy sample mounting, high resolution, and the low-phototoxicity of light sheet illumination paves the way for new dynamic studies in the brain of living flies.

MEDiCINe: Motion Correction for Neural Electrophysiology Recordings
Electrophysiology recordings from the brain using laminar multielectrode arrays allow researchers to measure the activity of many neurons simultaneously. However, laminar microelectrode arrays move relative to their surrounding neural tissue for a variety of reasons, such as pulsation, changes in intracranial pressure, and decompression of neural tissue after insertion. Inferring and correcting for this motion stabilizes the recording and is critical to identify and track single neurons across time. Such motion correction is a preprocessing step of standard spike sorting methods. However, estimating motion robustly and accurately in electrophysiology recordings is challenging due to the stochasticity of the neural data. To tackle this problem, we introduce MEDiCINe (Motion Estimation by Distributional Contrastive Inference for Neurophysiology), a novel motion estimation method. We show that MEDiCINe outperforms existing motion estimation methods on an extensive suite of simulated neurophysiology recordings and leads to more accurate spike sorting. We also show that MEDiCINe correctly estimates the motion in primate electrophysiology recordings with a variety of motion and stability statistics. We open-source MEDiCINe, usage instructions, examples integrating MEDiCINe with common tools for spike-sorting, and data and code for reproducing our results. This open software will enable other researchers to use MEDiCINe to improve spike sorting results and get the most out of their electrophysiology datasets.

Evaluating Synthetic Diffusion MRI Maps created with Diffusion Denoising Probabilistic Models
Generative AI models, such as Stable Diffusion, DALL-E, and MidJourney, have recently gained widespread attention as they can generate high-quality synthetic images by learning the distribution of complex, high-dimensional image data. These models are now being applied to medical and neuroimaging data, where AI-based tasks such as diagnostic classification and predictive modeling typically use deep learning methods, such as convolutional neural networks (CNNs) and vision transformers (ViTs), with interpretability enhancements. In our study, we trained latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) specifically for generating synthetic Diffusion Tensor Imaging (DTI) maps. We developed models that generate synthetic DTI maps of mean diffusivity by training on real 3D DTI scans, and evaluating the synthetic data's realism and diversity using maximum mean discrepancy (MMD) and multi-scale structural similarity index (MS-SSIM). Additionally, we aim to assess the transfer learning capability of a 3D CNN-based Alzheimer's disease classifier, by pretraining it on sex classification on real and synthetic DTIs. Our approach efficiently produces realistic and diverse synthetic data, potentially helping to create interpretable AI-driven maps for neuroscience research and clinical diagnostics.

Reducing CETP activity prevents memory decline in an Alzheimer's disease mouse model
Epidemiological studies have shown that lower activity of the cholesteryl ester transfer protein (CETP) correlates with reduced Alzheimer's disease (AD) risk. While small molecule CETP inhibitors like evacetrapib have previously been assessed for cardiovascular diseases, their involvement in AD has not been investigated. Here, we establish CETP as a novel pharmacological target for AD treatment. Using CETP transgenic mice crossed to a mouse model of amyloidosis and administering evacetrapib, we provide evidence that CETP inhibition maintained memory independent of classic AD markers, increased hippocampal cholesterol, altered plasma lipoproteins, and changed transcription of genes linked to brain barriers. Using proteomic data of cerebrospinal fluid from cognitively unimpaired individuals at risk for AD (the PREVENT-AD cohort), we confirm that our mouse model reflects physiological changes in pre-symptomatic human subjects. We propose the repurposing of CETP inhibitors as an effective therapeutic strategy to delay or prevent cognitive impairment in AD.

Loss of mitochondrial enzyme GPT2 leads to reprogramming of synaptic glutamate metabolism
Recessive loss-of-function mutations in the mitochondrial enzyme Glutamate Pyruvate Transaminase 2 (GPT2) cause intellectual disability in children. Given this cognitive disorder, and because glutamate metabolism is tightly regulated to sustain excitatory neurotransmission, here we investigate the role of GPT2 in synaptic function. GPT2 catalyzes a reversible reaction interconverting glutamate and pyruvate with alanine and alpha-ketoglutarate, a TCA cycle intermediate; thereby, GPT2 may play an important role in linking mitochondrial tricarboxylic acid (TCA) cycle with synaptic transmission. In mouse brain, we find that GPT2 is enriched in mitochondria of synaptosomes (isolated synaptic terminals). Loss of Gpt2 in mouse appears to lead to reprogramming of glutamate and glutamine metabolism, and to decreased glutamatergic synaptic transmission. Whole-cell patch-clamp recordings in pyramidal neurons of CA1 hippocampal slices from Gpt2-null mice reveal decreased excitatory post-synaptic currents (mEPSCs) without changes in mEPSC frequency, or importantly, changes in inhibitory post-synaptic currents (mIPSCs). Additional evidence of defective glutamate release included reduced levels of glutamate released from Gpt2-null synaptosomes measured biochemically. Glutamate release from synaptosomes was rescued to wild-type levels by alpha-ketoglutarate supplementation. Additionally, we observed evidence of altered metabolism in isolated Gpt2-null synaptosomes: decreased TCA cycle intermediates, and increased glutamate dehydrogenase activity. Notably, alterations in the TCA cycle and the glutamine pool were alleviated by alpha-ketoglutarate supplementation. In conclusion, our data support a model whereby GPT2 mitochondrial activity may contribute to glutamate availability in pre-synaptic terminals, thereby highlighting potential interactions between pre-synaptic mitochondrial metabolism and synaptic transmission.

Long-term effects of psilocybin on dynamic and effectivity connectivity of fronto-striatal-thalamic circuits
Psilocybin has been shown to induce fast and sustained improvements in mental well-being across various populations, yet its long-term mechanisms of action are not fully understood. Initial evidence suggests that longitudinal functional and structural brain changes implicate fronto-striatal-thalamic (FST) circuitry, a broad system involved in goal-directed behavior and motivational states. Here, we apply empirical methods and computational modeling to resting-state fMRI data from a within-subject longitudinal psilocybin trial in psychedelic-naive healthy volunteers. We first show increases in FST dynamic activity four weeks after a full dose of psilocybin. We then proceed to mechanistically account for these increased dynamics, by showing that reduced structural constraints underlie increased FST dynamic activity post psilocybin. Further, we show that these reduced structural constraints come along with increased bottom-up and reduced top-down modulation of FST circuits. While cortical reductions in top-down modulation are linked to regional 5-HT2A receptor availability, increased information outflow via subcortical and limbic regions relate to local D2 receptor availability. Together, these findings show that increased FST flexibility weeks after psilocybin administration is linked to serotonergic-mediated decreases in top-down information flow and dopaminergic-mediated increases in bottom-up information flow. This long-term functional re-organization of FST circuits may represent a common mechanism underling the potential clinical efficacy of psilocybin across various neuropsychiatric disorders including substance abuse, major depression, and anorexia.

Correction of RBFOX1 deficit rescues Huntington's disease mis-splicing and pathology
RNA mis-splicing correction therapies have been developed for neurological disorders like spinal muscular atrophy and neuronal ceroid lipofuscinosis. In Huntington's disease (HD), pathogenic mis-splicing was initially observed in genes linked to neurodegeneration, such as HTT itself, MAPT, and TAF1. Later, genome-wide analyses identified a broader mis-splicing signature in HD brains, involving additional neurodegeneration-related genes. Correcting each mis-spliced gene individually would be unfeasible, highlighting the need to target upstream splicing factors altered in HD. Our previous motif-enrichment analyses of intronic sequences flanking the exons mis-spliced in HD identified RBFOX and U2AF2 as candidate splicing factors, both of which are reduced in HD brains. In this study, we tested their pathogenic relevance generating conditional transgenic mouse models that overexpress RBFOX1 or U2AF2 in forebrain neurons and combining them with HD mice. Our results show that moderate overexpression of RBFOX1, but not U2AF2, corrects multiple HD-associated mis-splicing events and alleviates HD mice neuropathology and motor symptoms. These findings demonstrate that RBFOX1 downregulation contributes to HD pathology and underscore the therapeutic potential of strategies aimed at increasing RBFOX1 levels.